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A vae is a variational autoencoder 我在调试一个Diffusion模型的采样代码，就想过这个问题。 因为发现一个很有意思的现象——把采样步数从1000降到50，生成效果居然没啥区别。 这让我想起早些年折腾VAE时的困惑：为啥重参数化技巧能work？被GAN的mode collapse搞到崩溃的经历… 搞了这么多年生成模型，我越来越觉得， 这些看起来完全不. An autoencoder is a model (or part of a model) that is trained to produce its input as output
By giving the model less information to represent the data than the input contains, it's forced to learn about the input distribution and compress the information. A vae renders the image, the last step after all the ai magic SD原文3.1节中同时提供了VAE和VQ-VAE两种方案，VAE效果更好所以被大家一直沿用） 之所以效果这么好，主要还是因为diffusion model强大。
VAE、GAN 这种生成模型和 transformer 有什么区别？ 想问问为什么在分子生成中大部分选择使用VAE、GAN这种生成模型，而不使用transformer进行生成，这两种直接有什么本质的区别吗？ transfo… 显示全部 关注者 173
VAE生成例子 MNIST是个手写数字数据集，相信大家耳熟能详，就用这个数据集来解释VAE，网上代码很多，tensorflow的官方教程也包含了一个，这里就不再详细展开。 Trueyou seem to have some misconceptions when it comes to vae The vae is what gets you from latent space to pixelated images and vice versa There's hence no such thing as no vae as you wouldn't have an image
It hence would have used a default vae, in most cases that would be the one used for sd 1.5 A vae is hence also definitely not a network extension file Still one of the best ways. 最后说一下比较有意思的事： VAE、GAN、Flow (NICE)三种模型都是2013-2014年提出来的（VAE是13年放到arXiv上的，后来中了NIPS；GAN也同时中了NIPS，而NICE最早是14年的一个ICLR workshop）。 最后的发展情况是：GAN最火，VAE次之，Flow模型似乎总是要火不火。
If it had run out of memory earlier in the workflow it might have also recommended the vae encode tiled node.
VAE：使用变分推断 [1] 将标准正态分布变换到目标分布的概率图模型。 相较于GAN易于训练，但也有后验崩塌问题，同时由于ELBO只是目标分布对数似然的一个下界，因此VAE的生成结果通常会比较模糊。
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